At a Glance
- Tasks: Enhance trade surveillance with ML and NLP techniques, building and deploying innovative models.
- Company: TradingHub, a leading analytics firm in London with a focus on technology.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Other info: Join a high-performing team and collaborate across functions for scalable solutions.
- Why this job: Be the first dedicated ML hire and make a real impact in financial analytics.
- Qualifications: Experience in machine learning and natural language processing is essential.
The predicted salary is between 60000 - 80000 £ per year.
TradingHub, based in London, is seeking a Machine Learning Engineer to join the Analytics division and enhance our trade surveillance platform using modern ML and NLP techniques. You will build, deploy, and iterate models to deliver new capabilities for customers and help drive data-driven decisions.
This is the first dedicated ML hire, working with a high-performing team of Quant Researchers and Developers, and collaborating with cross-functional teams to productionise scalable solutions.
Machine Learning Engineer - Financial Analytics & NLP employer: eFinancialCareers
Quilter plc is an exceptional employer, offering a dynamic work environment in Southampton where innovation and collaboration thrive. With a strong commitment to employee growth, comprehensive benefits including a generous holiday allowance and a non-contributory pension scheme, Quilter fosters a culture of inclusivity and continuous improvement, empowering employees to make meaningful contributions to the financial futures of their clients and communities.
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We think this is how you could land Machine Learning Engineer - Financial Analytics & NLP
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We think you need these skills to ace Machine Learning Engineer - Financial Analytics & NLP
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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